Comparison of EEG Devices for Eye State Classification


In this paper, we investigate whether the price of an EEG device is directly correlated with the quality of the obtained data when applied to a simple classification task. The data of three different devices (one medical and two consumer) was used to determine the eye state (open or closed). For classification, 83 machine learning algorithms were used on the raw EEG data. While the cheapest device performed extremly poor with only one classifier better than the majority vote the other two devices achieved high accuracy. The lowest error rate for a more expensive consumer EEG was 1.38% and produced by KStar. For the medical device the best performing classifier was IBk which achieved an error rate of 1.63%. Except for KStar, the classifiers achieved a lower error rate by the medical EEG measurement system than the consumer EEG system.

In Proceedings of the AIHLS